2013 TranslatingEmbeddingsforModelin

• (Bordes et al., 2013) ⇒ Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, Jason Weston, and Oksana Yakhnenko. (2013). “Translating Embeddings for Modeling Multi-relational Data.” In: Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS-2013).

Subject Headings: TransE.

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Translating+Embeddings+for+Modeling+Multi-relational+Data
• http://dl.acm.org/citation.cfm?id=2999792.2999923&preflayout=flat#citedby

Quotes

Abstract

We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 TranslatingEmbeddingsforModelin	Jason Weston Nicolas Usunier Antoine Bordes Alberto Garcia-Durán Oksana Yakhnenko			Translating Embeddings for Modeling Multi-relational Data